Cortical excitability may be subject to changes through training and learning. Motor training can increase cortical excitability in motor cortex, and facilitation of motor cortical excitability has been shown to be positively correlated with improvements in performance in simple motor tasks. Thus cortical excitability may tentatively be considered as a marker of learning and use-dependent plasticity. Previous studies focused on changes in cortical excitability brought about by learning processes, however, the relation between native levels of cortical excitability on the one hand and brain activation and behavioral parameters on the other is as yet unknown. In the present study we investigated the role of differential native motor cortical excitability for learning a motor sequencing task with regard to post-training changes in excitability, behavioral performance and involvement of brain regions. Our motor task required our participants to reproduce and improvise over a pre-learned motor sequence. Over both task conditions, participants with low cortical excitability (CElo) showed significantly higher BOLD activation in task-relevant brain regions than participants with high cortical excitability (CEhi). In contrast, CElo and CEhi groups did not exhibit differences in percentage of correct responses and improvisation level. Moreover, cortical excitability did not change significantly after learning and training in either group, with the exception of a significant decrease in facilitatory excitability in the CEhi group. The present data suggest that the native, unmanipulated level of cortical excitability is related to brain activation intensity, but not to performance quality. The higher BOLD mean signal intensity during the motor task might reflect a compensatory mechanism in CElo participants.

You’ve likely heard that multitasking is problematic, but new studies show that it kills your performance and may even damage your brain. Research conducted at Stanford University found that multitasking is less productive than doing a single thing at a time. The researchers found that people who are regularly bombarded with several streams of electronic information cannot pay attention, recall information, or switch from one job to another as well as those who complete one task at a time.

Although price and quality play important roles in our decision to purchase a product, science suggests that the appearance of the person attempting to sell the goods also has a hand.

What goes through your head when deciding whether or not to purchase an item? It’s likely that quality and price are important factors, but what about the appearance of the model advertising the product or the salesperson attempting to sell it? Science suggests that these factors may be just as important as the product itself when it comes to the consumer’s decision to purchase.

We have a limited understanding of the factors that make people influential and topics popular in social media. Are users who comment on a variety of matters more likely to achieve high influence than those who stay focused? Do general subjects tend to be more popular than specific ones? Questions like these demand a way to detect the topics hidden behind messages associated with an individual or a keyword, and a gauge of similarity among these topics. Here we develop such an approach to identi

For infants, the first problem in learning a word is to map the word to its referent; a second problem is to remember that mapping when the word and/or referent are again encountered. Recent infant studies suggest that spatial location plays a key role in how infants solve both problems. Here we provide a new theoretical model and new empirical evidence on how the body – and its momentary posture – may be central to these processes. The present study uses a name-object mapping task in which names are either encountered in the absence of their target (experiments 1–3, 6 & 7), or when their target is present but in a location previously associated with a foil (experiments 4, 5, 8 & 9). A humanoid robot model (experiments 1–5) is used to instantiate and test the hypothesis that body-centric spatial location, and thus the bodies’ momentary posture, is used to centrally bind the multimodal features of heard names and visual objects. The robot model is shown to replicate existing infant data and then to generate novel predictions, which are tested in new infant studies (experiments 6–9). Despite spatial location being task-irrelevant in this second set of experiments, infants use body-centric spatial contingency over temporal contingency to map the name to object. Both infants and the robot remember the name-object mapping even in new spatial locations. However, the robot model shows how this memory can emerge –not from separating bodily information from the word-object mapping as proposed in previous models of the role of space in word-object mapping – but through the body’s momentary disposition in space.

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

Earlier this week, we went up to Boston to see something new from Rethink Robotics. They wouldn’t tell us what (not even a hint), but we bought plane tickets anyway, because Rodney Brooks told us that it wasn’t just some slightly different version of Baxter. And it wasn’t: it’s a completely different robot, stuffing all of the adaptive, collaborative technology that makes Baxter unique into a form factor that’s smaller, faster, stronger, and more precise.

How do we tell whether a proposed visualization is a valid pictorial representation of the truth or just an accidental but appealing image? Art and science can work brilliantly together in visualization science, but we must know when, and how, to distinguish them.

There are countless weather apps available for the iPhone, but how many of them feature sadistic robots that taunt you when it’s going to rain? Carrot Weather, which arrives in the iTunes Store on Thursday, is the latest addition to the popular app ecosystem centered around a digital assistant that’s more HAL 9000 than Siri.

Carrot is a sadistic supercomputer that gets you to do things like complete tasks, wake up and lose weight -- by berating you for your failures. She is essentially Stephen Hawking and Elon Musk's worst nightmare.

In reality, the Carrot suite of iPhone apps has become popular by hooking its “meatbags” (her preferred term for humans) on snarky quips and pop-culture references. Creator Brian Mueller is a self-taught programmer who left screenwriting to develop apps. He said the character is based on the sarcastic prodding he often encounters from his wife, sister and mom.

Predators make foraging decisions based upon sensory information about resource availability, but little is known about how large, air-breathing predators collect and use such information to maximize energy returns when foraging in the deep sea. Here, we used archival tags to study how echolocating sperm whales (Physeter macrocephalus) use their long-range sensory capabilities to guide foraging in a deep-water habitat consisting of multiple, depth-segregated prey layers. Sperm whales employ a directed search behaviour by modulating their overall sonar sampling with the intention to exploit a particular prey layer. They forage opportunistically during some descents while actively adjusting their acoustic gaze to sequentially track different prey layers. While foraging within patches, sperm whales adjust their clicking rate both to search new water volumes as they turn and to match the prey distribution. This strategy increases information flow and suggests that sperm whales can perform auditory stream segregation of multiple targets when echolocating. Such flexibility in sampling tactics in concert with long-range sensing capabilities apparently allow sperm whales to efficiently locate and access prey resources in vast, heterogeneous, deep water habitats.

oogle’s Pagerank algorithm has become one of the most famous in computer science. It was originally designed to rank websites according to their importance by assuming that a site is important if it is linked to by other important sites.

The algorithm works by counting the links to a website and the importance of the sites these come from. It then uses this to work out the importance of the original site. Through a process of iteration, the algorithm comes up with a ranking.

Since Google’s founders, Larry Page and Sergei Brin, developed the algorithm in the mid-1990s, researchers have begun using it to rank nodes in other networks. One idea has been to use it to rank scientific papers using the network of links in the references they contain. Another is to use it for elections in which everyone is a candidate and can vote for anybody else.

Ashish Umre's insight:

Ref: arxiv.org/abs/1503.01331 PageRank Approach to Ranking National Football Teams

@deerwalkinc Google’s Pagerank algorithm has become one of the most famous in computer science. It was originally designed to rank websites according to their importance by assuming that a site is important if it is linked to by other important sites

People generally fail to produce random sequences by overusing alternating patterns and avoiding repeating ones—the gambler’s fallacy bias. We can explain the neural basis of this bias in terms of a biologically motivated neural model that learns from errors in predicting what will happen next. Through mere exposure to random sequences over time, the model naturally develops a representation that is biased toward alternation, because of its sensitivity to some surprisingly rich statistical structure that emerges in these random sequences. Furthermore, the model directly produces the best-fitting bias-gain parameter for an existing Bayesian model, by which we obtain an accurate fit to the human data in random sequence production. These results show that our seemingly irrational, biased view of randomness can be understood instead as the perfectly reasonable response of an effective learning mechanism to subtle statistical structure embedded in random sequences.

Today, computer vision systems are tested by their accuracy in detecting and localizing instances of objects. As an alternative, and motivated by the ability of humans to provide far richer descriptions and even tell a story about an image, we construct a “visual Turing test”: an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query engine proposes a question; the operator either provides the correct answer or rejects the question as ambiguous; the engine proposes the next question (“just-in-time truthing”). The test is then administered to the computer-vision system, one question at a time. After the system’s answer is recorded, the system is provided the correct answer and the next question. Parsing is trivial and deterministic; the system being tested requires no natural language processing. The query engine employs statistical constraints, learned from a training set, to produce questions with essentially unpredictable answers—the answer to a question, given the history of questions and their correct answers, is nearly equally likely to be positive or negative. In this sense, the test is only about vision. The system is designed to produce streams of questions that follow natural story lines, from the instantiation of a unique object, through an exploration of its properties, and on to its relationships with other uniquely instantiated objects.

This study investigated whether eye contact perception differs in people with different cultural backgrounds. Finnish (European) and Japanese (East Asian) participants were asked to determine whether Finnish and Japanese neutral faces with various gaze directions were looking at them. Further, participants rated the face stimuli for emotion and other affect-related dimensions. The results indicated that Finnish viewers had a smaller bias toward judging slightly averted gazes as directed at them when judging Finnish rather than Japanese faces, while the bias of Japanese viewers did not differ between faces from their own and other cultural backgrounds. This may be explained by Westerners experiencing more eye contact in their daily life leading to larger visual experience of gaze perception generally, and to more accurate perception of eye contact with people from their own cultural background particularly. The results also revealed cultural differences in the perception of emotion from neutral faces that could also contribute to the bias in eye contact perception.

The family of animal robots created by German robotics company Festo is growing. As part of its Bionic Learning Network, the company has introduced two new robots: a swarm of ants that can operate cooperatively, and a butterfly robot that leverages the insect's lightness.

The ant robots -- called BionicANTS -- are not just inspired by the insect's physical body, but by its swarm intelligence.

Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification – locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.

"There was a catch, though: This symbolic abstraction made the world transparent but the brain opaque. Once everything had been reduced to information governed by logic, the actual mechanics ceased to matter—the tradeoff for universal computation was ontology. Von Neumann was the first to see the problem. He expressed his concern to Wiener in a letter that anticipated the coming split between artificial intelligence on one side and neuroscience on the other. “After the great positive contribution of Turing-cum-Pitts-and-McCulloch is assimilated,” he wrote, “the situation is rather worse than better than before. Indeed these authors have demonstrated in absolute and hopeless generality that anything and everything … can be done by an appropriate mechanism, and specifically by a neural mechanism—and that even one, definite mechanism can be ‘universal.’ Inverting the argument: Nothing that we may know or learn about the functioning of the organism can give, without ‘microscopic,’ cytological work any clues regarding the further details of the neural mechanism."

A team of researchers has found that posture is critical in the early stages of acquiring new knowledge. Using both robots and infants, researchers examined the role bodily position played in the brain's ability to "map" names to objects. They found that consistency of the body's posture and spatial relationship to an object as an object's name was shown and spoken aloud were critical to successfully connecting the name to the object. The study offers a new approach to studying the way "objects of cognition," such as words or memories of physical objects, are tied to the position of the body. The new insights stem from the field of epigenetic robotics, in which researchers are working to create robots that learn and develop like children, through interaction with their environment.

Alex Endert's dissertation "Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering" described semantic interaction, a user interaction methodology for visual analytics (VA). It showed that user interaction embodies users' analytic process and can thus be mapped to model-steering functionality for "human-in-the-loop" system design. The dissertation contributed a framework (or pipeline) that describes such a process, a prototype VA system to test semantic interaction, and a user evaluation to demonstrate semantic interaction's impact on the analytic process. This research is influencing current VA research and has implications for future VA research.

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.

When you chat with friends about settling debts or splitting the bill, Facebook doesn’t want you to have to open another app like PayPal or Venmo to send them money. So today it unveiled a new payments feature for Facebook Messenger that lets you connect your Visa or Mastercard debit card and tap a “$” button to send friends money on iOS, Android, and desktop with zero fees. Facebook Messenger payments will roll out first in the U.S. over the coming months.

Ye et al. (1) address a critical problem confronting the management of natural ecosystems: How can we make forecasts of possible future changes in populations to help guide management actions? This problem is especially acute for marine and anadromous fisheries, where the large interannual fluctuations of populations, arising from complex nonlinear interactions among species and with varying environmental factors, have defied prediction over even short time scales. The empirical dynamic modeling (EDM) described in Ye et al.’s report, the latest in a series of papers by Sugihara and his colleagues, offers a promising quantitative approach to building models using time series to successfully project dynamics into the future.

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

@deerwalkinc #diduknow oogle’s We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks

We tend to think that everyone deserves an equal say in a debate. This seemingly innocuous assumption can be damaging when we make decisions together as part of a group. To make optimal decisions, group members should weight their differing opinions according to how competent they are relative to one another; whenever they differ in competence, an equal weighting is suboptimal. Here, we asked how people deal with individual differences in competence in the context of a collective perceptual decision-making task. We developed a metric for estimating how participants weight their partner’s opinion relative to their own and compared this weighting to an optimal benchmark. Replicated across three countries (Denmark, Iran, and China), we show that participants assigned nearly equal weights to each other’s opinions regardless of true differences in their competence—even when informed by explicit feedback about their competence gap or under monetary incentives to maximize collective accuracy. This equality bias, whereby people behave as if they are as good or as bad as their partner, is particularly costly for a group when a competence gap separates its members.

The emergence and sustenance of cooperative behavior is fundamental for a society to thrive. Recent experimental studies have shown that cooperation increases in dynamic networks in which subjects can choose their partners. However, these studies did not vary reputational knowledge, or what subjects know about other’s past actions, which has long been recognized as an important factor in supporting cooperation. They also did not give subjects access to global social knowledge, or information on who is connected to whom in the group. As a result, it remained unknown how reputational and social knowledge foster cooperative behavior in dynamic networks both independently and by complementing each other. In an experimental setting, we show that global reputational knowledge is crucial to sustaining a high level of cooperation and welfare. Cooperation is associated with the emergence of dense and clustered networks with highly cooperative hubs. Global social knowledge has no effect on the aggregate level of cooperation. A community analysis shows that the addition of global social knowledge to global reputational knowledge affects the distribution of cooperative activity: cooperators form a separate community that achieves a higher cooperation level than the community of defectors. Members of the community of cooperators achieve a higher payoff from interactions within the community than members of the less cooperative community.

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